Explicating feature contribution using Random Forest proximity distances

17 July 2018

Papers citing "Explicating feature contribution using Random Forest proximity distances"

10 / 10 papers shown

Title
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR Sandra Wachter Brent Mittelstadt Chris Russell MLAU 104 2,352 0 01 Nov 2017
Probability Series Expansion Classifier that is Interpretable by Design S. Agarwal Corey M. Hudson 21 3 0 27 Oct 2017
Consistent feature attribution for tree ensembles Scott M. Lundberg Su-In Lee FAtt 24 119 0 19 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,864 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,871 0 10 Apr 2017
Mapping chemical performance on molecular structures using locally interpretable explanations Leanne S. Whitmore Anthe George Corey M. Hudson FAtt 31 12 0 22 Nov 2016
Evaluating Causal Models by Comparing Interventional Distributions Dan Garant David D. Jensen CML 108 11 0 16 Aug 2016
Model-Agnostic Interpretability of Machine Learning Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 84 838 0 16 Jun 2016
The Mythos of Model Interpretability Zachary Chase Lipton FaML 180 3,699 0 10 Jun 2016
Testing Identifiability of Causal Effects D. Galles Judea Pearl CML 60 90 0 20 Feb 2013